Signatures of antisociality, brain structure and social exposome in early psychosis and depression
Poster No:
531
Submission Type:
Abstract Submission
Authors:
Clara Weyer1, Clara Vetter2, Unn Kristin Haukvik3, David Popovic4, Nikolaos Koutsouleris5
Institutions:
1LMU Munich, Munich, Bavaria, 2University of Munich, Munich, Bavaria, 3University of Oslo, Oslo, Oslo, 4Max Planck Institute, Ludwig-Maximillian University, Münich, Bavaria, 5University of Munich, Munich, Germany
First Author:
Co-Author(s):
Introduction:
Antisocial behavior is a multifaceted phenomenon influenced by the interplay of brain structure and social environmental factors across diagnostic boundaries. Investigating these interactions is crucial for understanding their role in clinical trajectories, necessitating advanced mathematical modeling. Therefore, we present a novel multivariate machine learning approach to elucidate the clinical and neuroanatomical complexity of antisocial behavior in a transdiagnostic cohort of adolescents and young adults.
Methods:
We used data from the prospective, multicentric European Personalized Prognostic Tools for Early Psychosis Management (PRONIA) project, comprising 707 minimally medicated participants (mean (SD) age = 25.4 (5.91)), including individuals with recent-onset depression (n = 238) or psychosis (n = 244) and clinical high-risk states for psychosis (n = 225). To detect parsimonious associations between antisocial behavior (PANSS items P4, P7, G4, G8, G14), social exposome (CTQ, EDS, BS, LEE, MSPSS, RSA) and brain volume (GMV, WMV, CSF), the multi-block sparse partial least squares algorithm was employed within a nested cross-validation framework.
Results:
The analysis yielded two significant signatures. The first (P = 0.003, Frobenius norm = 2.32) captured an ageing-related GMV pruning pattern of GMV reduction in frontal and parietal regions. The second (P = 0.01, Frobenius norm = 2.22) linked higher levels of hostility (P7) and excitement (P4) and experience of past and present discrimination and childhood trauma (sexual and physical abuse) to GMV reductions in predominantly frontal areas and enlargement of the third ventricle. This finding was particularly pronounced in younger individuals with recent-onset psychosis.
Conclusions:
This study presents the first application of the multi-block sparse partial least squares approach to integrate multiple clinical and neurobiological domains in the study of antisocial behavior in early-stage mental disorders. The multi-level signature linking antisocial traits, frontal GMV reductions, and social adversities in younger individuals with recent-onset psychosis highlights the complex, multifactorial etiology of antisocial behavior in first-episode psychosis. It further emphasizes the need for early assessment, targeted intervention, and long-term risk management in specifically identified clinical subpopulations.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Multivariate Approaches 2
Keywords:
Affective Disorders
Machine Learning
Multivariate
Psychiatric Disorders
Social Interactions
STRUCTURAL MRI
Trauma
Other - Antisociality
1|2Indicates the priority used for review
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Montoya, A., Valladares, A., Lizán, L., San, L., Escobar, R., & Paz, S. (2011). Validation of the Excited Component of the Positive and Negative Syndrome Scale (PANSS-EC) in a naturalistic sample of 278 patients with acute psychosis and agitation in a psychiatric emergency room. Health and quality of life outcomes, 9, 18. https://doi.org/10.1186/1477-7525-9-18
Popovic, D., Ruef, A., Dwyer, D. B., Antonucci, L. A., Eder, J., Sanfelici, R., Kambeitz-Ilankovic, L., Oztuerk, O. F., Dong, M. S., Paul, R., Paolini, M., Hedderich, D., Haidl, T., Kambeitz, J., Ruhrmann, S., Chisholm, K., Schultze-Lutter, F., Falkai, P., Pergola, G., Blasi, G., … PRONIA Consortium (2020). Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes. Biological psychiatry, 88(11), 829–842. https://doi.org/10.1016/j.biopsych.2020.05.020